The genetic analysis of repeated measures. II the Karhunen-Loève expansion (original) (raw)
The genetic analysis of repeated measures. II the Karhunen-Lo�ve expansion
Behavior Genetics, 1987
A new approach to the genetic analysis of time series of arbitrary length and with arbitrary covariance function is outlined. This approach is based on the simultaneous eigenvalue decomposition of the covariance matrices of the original time series obtained from monozygotic (MZ) and dizygotic (DZ) twins. The method is illustrated with computer-simulated twin data.
The genetic analysis of repeated measures. I. Simplex models
Behavior Genetics, 1987
The well-known simplex model is extended to a model that may be used for the genetic and environmental analysis of covariance structures. This "double" simplex structure can be specified as a LISREL model. It is shown that data which give rise to a simplex correlation structure, such as repeated-measures data, do not fit a factor-analysis model. The parameter estimation of the simplex model is illustrated with computersimulated twin data.
Behavior Genetics, 1991
A longitudinal model based on the simplex model is presented to analyze simultaneously means and covariance structure using univariate longitudinal twin data. The objective of the model is to decompose the mean trend into components which can be attributed to those genetic and environmental factors which give rise to phenotypic individual differences and a component of unknown constitution which does not involve individual differences. Illustrations are given using simulated data and repeatedly measured weight obtained in a sample of 82 female twin pairs on sbc occasions.
Decomposition of multivariate phenotypic means in multigroup genetic covariance structure analysis
Behavior Genetics, 1992
Observed differences in phenotypic means between groups such as parents and their offspring or male and female twins can be decomposed into genetic and environmental components. The decomposition is based on the assumption that the difference in phenotypic means is due to a difference in the location of the normal genetic and environmental distributions underlying the phenotypic individual differences. Differences between the groups in variance can be accommodated insofar as they are due to differences in unique variance or can be modeled using a scale parameter. The decomposition may be carried out in the standard analysis of genetic covariance structure using, for instance, LISREL. Illustrations are given using simulated data and twin data relating to blood pressure. Other possible applications are mentioned. KEY WORDS: group differences in phenotypic means; genetic means; environmental means; genetic and environmental covariance structure; twin data; parent-offspring data.
Genetics, 2000
The genetic analysis of characters that are best considered as functions of some independent and continuous variable, such as age, can be a complicated matter, and a simple and efficient procedure is desirable. Three methods are common in the literature: random regression, orthogonal polynomial approximation, and character process models. The goals of this article are (i) to clarify the relationships between these methods; (ii) to develop a general extension of the character process model that relaxes correlation stationarity, its most stringent assumption; and (iii) to compare and contrast the techniques and evaluate their performance across a range of actual and simulated data. We find that the character process model, as described in 1999 by Pletcher and Geyer, is the most successful method of analysis for the range of data examined in this study. It provides a reasonable description of a wide range of different covariance structures, and it results in the best models for actual data. Our analysis suggests genetic variance for Drosophila mortality declines with age, while genetic variance is constant at all ages for reproductive output. For growth in beef cattle, however, genetic variance increases linearly from birth, and genetic correlations are high across all observed ages.
Methodology for genetic studies of twins and families
1992
Few would dispute the truth of the statementPeople are Different', but there is much controversy over why. This book authoritatively explains the methods used to understand human variation, and extends them far beyond the primarynature or nurture'question. After chapters on basic statistics, biometrical genetics, matrix algebra and path analysis, there is a state-of-the-art account of how to fit genetic models using the LISREL package. The authors explain not only the assumptions of the twin method, but how to test them.
Spectral Analysis of Twin Time Series Designs
Acta geneticae medicae et gemellologiae: twin research, 1987
The genetic analysis of physiological time series has to accommodate the presence of autocorrelation. This can be accomplished by means of orthogonal transformation of the series, thus enabling the use of standard genetic analysis techniques for the sequence of uncorrelated transforms. In view of the oscillatory character which typifies various physiological time series, it is customary to invoke spectral techniques for the analysis of these series. It can be shown that spectral analysis is an orthogonal transformation that asymptotically resembles principal component analysis. Consequently, standard genetic analysis methods for the uncorrelated spectral transforms may be used. This approach will be illustrated with simulated and real (heart rate) data for univariate twin time series. Furthermore, it will be indicated that the proposed analysis can be readily generalized to multivariate time series.
Statistical Analysis of Genetic Data in Twin Studies and Association Studies
Vrije Universiteit: Amsterdam, 2007
Abstract: In studies in human genetics we want to answer questions such as: how important are genetic effects on a phenotype; what kind of action and interaction exists between gene products in the pathways between genotypes and phenotype; are the genetic effects on a ...
Genetic determination of the human EEG
Human Genetics, 1988
In this article, we have discussed recent progress in quantifying the genetically determined component of the resting EEG. This progress has been made possible in particular by the application of advanced information processing techniques such as "supervised learning," and the development of a problem-oriented "similarity" concept. Our work aimed at modeling previous findings regarding the distinct individuality of human brainwave patterns, the high similarity between the EEGs of monozygotic twins, and the average within-pair similarity of dizygotic twins. Thus, we had three objectives: First, we wanted to improve the quantification of EEG characteristics with respect to reproducibility and specificity by means of adaptive procedures and repeated measurements. Second, we wanted to compare the "typical" within-subject EEG similarity with the "typical" within-pair EEG similarity of monozygotic and dizygotic twins brought up together. Finally, we were interested in the degree to which environmental factors affect the characteristics of human brainwave patterns. Our investigations were based on the empirical data derived from five different populations: (1) 81 healthy subjects, (2) 24 pairs of monozygotic twins brought up together, (3) 25 pairs of dizygotic twins brought up together, (4) 28 pairs of monozygotic twins reared apart, and (5) 21 pairs of dizygotic twins reared apart. Following our similarity conception, repeated measurements on the set of 81 individuals were used as design samples, and new registrations from the same individuals taken 14 days later were referred to as test samples in order to develop the appropriate method and to determine all required calibration parameters. This specific approach allowed us to construct EEG spectral patterns which, with a specificity and reproducibility of > 90% each, largely met the requirements of genetic EEG studies. Hence, we were able systematically to investigate the within-pair EEG similarity of our twin samples. Our results provided ample evidence that the individual characteristics of the resting EEG are primarily determined by genetic factors: (1) There exists an almost perfect one-to-one mapping between each individual and his EEG; (12) monozygotic twins proved, with respect to their resting EEGs, to be only slightly less like one another (if there is any difference at all) than each person is to himself over time; (3) the average withinpair EEG similarity estimated from a sufficiently representative sample of dizygotic twins is significantly above the interindividual EEG similarity between unrelated persons (this finding holds true for both samples of dizygotic twins brought Offprint requests to: H. H. Stassen up together and reared apart, and there is also no statistically significant difference in the resting EEG between these two samples) and, (4) the EEGs of monozygotic twins reared apart are obviously as similar to each other as are the EEGs of the same person over time, and there is no statistically significant difference in the resting EEG between the two populations of monozygotic twins brought up together and monozygotic twins reared apart.
Longitudinal analytical approaches to genetic data
Background: Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure–related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.